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vutuanhai237 · Sep 19, 2023 · 1096b93 · 1096b93
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diff --git a/.gitignore b/.gitignore
@@ -5,7 +5,6 @@ __pycache__/
 **/.DS_Store
 # C extensions
 *.so
-./codes/qtm
 # Distribution / packaging
 .Python
 build/

diff --git a/qtm/base.py b/qtm/base.py
@@ -299,102 +299,6 @@ def fit_state_preparation(u: types.FunctionType,
     else:
         return thetas, loss_values
 
-# def fit_state_tomography(u: types.FunctionType,
-#                           vdagger: qiskit.QuantumCircuit,
-#                           thetas: np.ndarray,
-#                           num_steps: int,
-#                           loss_func: types.FunctionType,
-#                           optimizer: types.FunctionType,
-#                           verbose: int = 0,
-#                           is_return_all_thetas: bool = False,
-#                           **kwargs):
-#     """Return the new thetas that fit with the circuit from create_vdagger_func function
-
-#     Args:
-#         - u (QuantumCircuit): fitting circuit
-#         - create_vdagger_func (types.FunctionType): added circuit function
-#         - thetas (np.ndarray): parameters
-#         - num_steps (Int): number of iterations
-#         - loss_func (types.FunctionType): loss function
-#         - optimizer (types.FunctionType): otimizer function
-#         - verbose (Int): the seeing level of the fitting process (0: nothing, 1: progress bar, 2: one line per step)
-#         - **kwargs: additional parameters for create_circuit_func()
-
-#     Returns:
-#         - thetas (np.ndarray): the optimized parameters
-#         - loss_values (np.ndarray): the list of loss_value
-#     """
-#     if verbose == 1:
-#         bar = qtm.progress_bar.ProgressBar(max_value=num_steps, disable=False)
-#     thetass = []
-#     loss_values = []
-#     n = vdagger.num_qubits
-#     uvaddager = u.compose(vdagger)
-#     for i in range(0, num_steps):
-#         grad_loss = qtm.base.grad_loss(
-#             u, create_vdagger_func, thetas, **kwargs)
-#         optimizer_name = optimizer.__name__
-
-#         if optimizer_name == 'sgd':
-#             thetas = qtm.optimizer.sgd(thetas, grad_loss)
-
-#         elif optimizer_name == 'adam':
-#             if i == 0:
-#                 m, v = list(np.zeros(thetas.shape[0])), list(
-#                     np.zeros(thetas.shape[0]))
-#             thetas = qtm.optimizer.adam(thetas, m, v, i, grad_loss)
-
-#         elif 'qng' in optimizer_name:
-#             grad_psi1 = qtm.base.grad_psi(u,
-#                                  create_vdagger_func,
-#                                  thetas,
-#                                  r=qtm.constant.two_term_psr['s'],
-#                                  s=np.pi,
-#                                  **kwargs)
-#             u_copy = create_vdagger_func(u.copy(), thetas, **kwargs)
-#             psi = qiskit.quantum_info.Statevector.from_instruction(u_copy).data
-#             psi = np.expand_dims(psi, 1)
-#             if optimizer_name == 'qng_fubini_study':
-#                 G = qtm.fubini_study.qng(
-#                     u.copy(), thetas, create_vdagger_func, **kwargs)
-#                 thetas = qtm.optimizer.qng_fubini_study(thetas, G, grad_loss)
-#             if optimizer_name == 'qng_fubini_hessian':
-#                 G = qtm.fubini_study.qng_hessian(
-#                     u.copy(), thetas, create_vdagger_func, **kwargs)
-#                 thetas = qtm.optimizer.qng_fubini_study(thetas, G, grad_loss)
-#             if optimizer_name == 'qng_fubini_study_scheduler':
-#                 G = qtm.fubini_study.qng(
-#                     u.copy(), thetas, create_vdagger_func, **kwargs)
-#                 thetas = qtm.optimizer.qng_fubini_study_scheduler(thetas, G, grad_loss, i)
-#             if optimizer_name == 'qng_qfim':
-#                 thetas = qtm.optimizer.qng_qfim(
-#                     thetas, psi, grad_psi1, grad_loss)
-#             if optimizer_name == 'qng_adam':
-#                 if i == 0:
-#                     m, v = list(np.zeros(thetas.shape[0])), list(
-#                         np.zeros(thetas.shape[0]))
-#                 thetas = qtm.optimizer.qng_adam(
-#                     thetas, m, v, i, psi, grad_psi1, grad_loss)
-#         else:
-#             thetas = optimizer(thetas, grad_loss)
-#         u_copy = create_vdagger_func(u.copy(), thetas, **kwargs)
-#         loss = loss_func(
-#             qtm.base.measure(u_copy, list(range(u_copy.num_qubits))))
-#         loss_values.append(loss)
-#         thetass.append(thetas.copy())
-#         if verbose == 1:
-#             bar.update(1)
-#         if verbose == 2 and i % 10 == 0:
-#             print("Step " + str(i) + ": " + str(loss))
-#     if verbose == 1:
-#         bar.close()
-
-#     if is_return_all_thetas:
-#         return thetass, loss_values
-#     else:
-#         return thetas, loss_values
-
-
 def fit(u: typing.Union[qiskit.QuantumCircuit, types.FunctionType], v: typing.Union[qiskit.QuantumCircuit, types.FunctionType],
         thetas: np.ndarray,
         num_steps: int,